MODELING OF SOCIO-DEMOGRAPHIC DEVELOPMENT IN THE SETTLEMENT SYSTEM OF CROSS-BORDER REGIONS OF THE U.S.A.
Bibliographic record
Abstract
The prediction of socio-demographic development of American transboundary regions as complex open mesoterritorial systems, historically and functionally based on the strengthening of diverse interaction between the United States and neighboring countries (Mexico and Canada), is relevant and practically significant in the design of changes in the settlement system of these territories. On the basis of unified system and meso-economic spatio-temporal modeling used in the present work, the author combined methods of multivariate analysis, linear programming, correlation calculations and others, adequate to the methodology and technique of socio-demographic forecasting. According to the results of an empirical study, hypotheses were confirmed about the possibility of a gradual escalation in the next decade of demographic and ethno-cultural turbulence in the settlement system of the trans-border territories of the southwestern United States bordering Mexico into the initial stage of regionalization caused by the projected increase in migration from Latin American countries; about an increase in imbalance and spatial inequality, reducing optimization in the ratio of the able-bodied population; in the conditions of a new technological order; on the increasing role of mutual influence of settlements of trans-regional regions of the USA, which form the material and territorial basis of the settlement system. The results of regression modeling of prospective changes in quantitative indicators of natural and mechanical population growth of a particular locality that has its rank in the settlement system of a cross-border region of the USA have shown that the qualitative nature and direction of dynamics of elements (objects, processes, environment, projects) of socio-demographic development depend on the size of the city center, neighboring settlements and suburbanized zones and the interactions between them.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".